Components

After some research I ordered:

• A microSD card reader/writer with SPI interface (Catalex card)
• A Bosch BME-280 temperature/pressure/humidity sensor with I2C interface

As the BME-280 sensor operates at 3.3V and my Arduino Nano at 5V, I also ordered a four channel Logic Level Converter to convert the 5V I2C on the Arduino side of the LLC to 3.3V on the BME-280 side.

To make the circuit Mains powered, i took an old Samsung mobile phone Charger (5V 0.7A), cutoff the plug and attached it to the breadboard.

Circuit & Programming

The breadboard layout (created using Fritzing) is shown below:

At first i was using the Arduino 5V pin (with Arduino connected to USB at the front of my Desktop PC, these USB ports might have lower current) to power both the SD card and the Level converter. Separately they would work fine, but together in one circuit the SD card gave erratic results. I guessed that current consumption was too high, and during testing I used the 5V charger as power supply for the SD card. During actual usage I used the 5V charger to power both the SD card AND the Arduino Nano, which worked nicely.

Coding was simple, i just combined the example code and libraries for a SPI SD card and for a BME-280 I2C sensor. I put the code on GitHub anyway as a reference.

Data collection and preparation

I ended up testing the device by letting it collect measurements in four different places within the house. In the following order:

• The living room
• The basement
• First floor bedroom
• First floor bathroom

After collecting the data I put the microSD card in a microSD card reader and copied the DATALOG.TXT CSV file to my pc for analysis in R.

df <- read.csv2("DATALOG.TXT", header = F)
colnames(df) <- c("Time", "Temp", "Hum", "Pressure")
# give the four traces a unique ID
df$start_trace <- ifelse(df$Time == 0, 1, 0)
df$trace_id <- cumsum(df$start_trace)

mdf <- melt(df, id.vars = c("Time", "trace_id"))

## Warning in melt(df, id.vars = c("Time", "trace_id")): The melt generic in
## data.table has been passed a data.frame and will attempt to redirect to the
## relevant reshape2 method; please note that reshape2 is deprecated, and this
## redirection is now deprecated as well. To continue using melt methods from
## reshape2 while both libraries are attached, e.g. melt.list, you can prepend the
## namespace like reshape2::melt(df). In the next version, this warning will become
## an error.

## Warning: attributes are not identical across measure variables; they will be
## dropped

# label the four traces
trace_id <- 1:4
trace_name <- c("Living room", "Basement", 
                "Bedroom 1st floor",  "Bathroom 1st floor")

cod <- data.table(trace_id, trace_name = 
                    factor(trace_name, levels = trace_name))

mdf <- data.table(merge(mdf, cod, by = "trace_id"))
mdf <- mdf[, value := as.numeric(value)]

Analysis

ggplot(mdf[mdf$variable == "Pressure" & Time > 1], 
       aes(x = Time, y = value, 
           color = variable, group = variable)) +
  geom_point(col = "grey") + 
  facet_grid(~ trace_name) + geom_smooth(size = 1)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(mdf[mdf$variable == "Temp" & Time > 1], 
       aes(x = Time, y = value, 
           color = variable, group = variable)) +
  geom_point() + facet_grid(~ trace_name)

ggplot(mdf[mdf$variable == "Hum" & Time > 1], 
       aes(x = Time, y = value, color = variable, 
           group = variable)) +
  geom_point() + facet_grid(~ trace_name)

Conclusion

It all seems to work pretty well. Measurement quality appears reasonable, with temperature and humidity consistent and with little noise, whereas the pressure reading needs some averaging / smoothing to get a stable signal.

I had great fun making this device!